Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes.

ABSTRACT: Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years; time since stroke 12.2±0.2 months;

SUBMITTER: Olafson ER 

PROVIDER: S-EPMC10491132 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature